{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Financial Data Structures: mlfinlab\n",
    "\n",
    "The purpose of this notebook is to act as a tutorial to bridge the gap between idea and implementation. In particular we will be looking at how to create the various financial data structures and how to format your data so that you can make use of the mlfinlab package.\n",
    "\n",
    "For this tutorial we made use of the sample data provided by TickWrite LLC. Using S&P500 E-mini futures. https://s3-us-west-2.amazonaws.com/tick-data-s3/downloads/ES_Sample.zip\n",
    "\n",
    "Note: we have also included the zip file in this repo as ES_Trades.csv.zip. You will need to unzip this file to use it in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlfinlab as ml\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Price</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Market Flag</th>\n",
       "      <th>Sales Condition</th>\n",
       "      <th>Exclude Record Flag</th>\n",
       "      <th>Unfiltered Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ESU13</td>\n",
       "      <td>09/01/2013</td>\n",
       "      <td>17:00:00.083</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>8</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1640.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ESU13</td>\n",
       "      <td>09/01/2013</td>\n",
       "      <td>17:00:00.083</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1640.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ESU13</td>\n",
       "      <td>09/01/2013</td>\n",
       "      <td>17:00:00.083</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>2</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1640.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ESU13</td>\n",
       "      <td>09/01/2013</td>\n",
       "      <td>17:00:00.083</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1640.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ESU13</td>\n",
       "      <td>09/01/2013</td>\n",
       "      <td>17:00:00.083</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>1</td>\n",
       "      <td>E</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1640.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Symbol        Date          Time    Price  Volume Market Flag  \\\n",
       "0  ESU13  09/01/2013  17:00:00.083  1640.25       8           E   \n",
       "1  ESU13  09/01/2013  17:00:00.083  1640.25       1           E   \n",
       "2  ESU13  09/01/2013  17:00:00.083  1640.25       2           E   \n",
       "3  ESU13  09/01/2013  17:00:00.083  1640.25       1           E   \n",
       "4  ESU13  09/01/2013  17:00:00.083  1640.25       1           E   \n",
       "\n",
       "   Sales Condition  Exclude Record Flag  Unfiltered Price  \n",
       "0                0                  NaN           1640.25  \n",
       "1                0                  NaN           1640.25  \n",
       "2                0                  NaN           1640.25  \n",
       "3                0                  NaN           1640.25  \n",
       "4                0                  NaN           1640.25  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "data = pd.read_csv('./tutorial_data/ES_Trades/ES_Trades.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "\n",
    "## Format Data\n",
    "\n",
    "Many data vendors will let you choose the format of your raw tick data files. We want to only focus on the following 3 columns: date_time, price, volume. The reason for this is to minimise the size of the csv files and the amount of time when reading in the files.\n",
    "\n",
    "Our data is sourced from TickData LLC which provide TickWrite 7, to aid in the formatting of saved files. This allows us to save csv files in the format date_time, price, volume.\n",
    "\n",
    "For this tutorial we will assume that you need to first do some preprocessing and then save your data to a csv file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      date    price  volume\n",
      "0  09/01/2013 17:00:00.083  1640.25       8\n",
      "1  09/01/2013 17:00:00.083  1640.25       1\n",
      "2  09/01/2013 17:00:00.083  1640.25       2\n",
      "3  09/01/2013 17:00:00.083  1640.25       1\n",
      "4  09/01/2013 17:00:00.083  1640.25       1\n",
      "\n",
      "\n",
      "Rows: 5454950\n"
     ]
    }
   ],
   "source": [
    "# Format the Data\n",
    "date_time = data['Date'] + ' ' + data['Time'] # Dont convert to datetime here, it will take forever to convert.\n",
    "new_data = pd.concat([date_time, data['Price'], data['Volume']], axis=1)\n",
    "new_data.columns = ['date', 'price', 'volume']\n",
    "print(new_data.head())\n",
    "print('\\n')\n",
    "print('Rows:', new_data.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save to csv\n",
    "\n",
    "Initially, your instinct may be to pass mlfinlab package an in-memory DataFrame object but the truth is when you're running the function in production, your raw tick data csv files will be way too large to hold in memory. We used the subset 2011 to 2019 and it was more than 25 gigs. It is for this reason that the mlfinlab package requires a file path to read the raw data files from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to csv\n",
    "new_data.to_csv('./tutorial_data/raw_tick_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Use mlfinlab: Create Data Structures\n",
    "Next we create the various data structures. In this example we focus on the standard bar types but the information driven bars are also available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Dollar Bars\n",
      "Reading data in batches:\n",
      "Batch number: 0\n",
      "Batch number: 1\n",
      "Batch number: 2\n",
      "Batch number: 3\n",
      "Batch number: 4\n",
      "Batch number: 5\n",
      "Returning bars \n",
      "\n",
      "Creating Volume Bars\n",
      "Creating Tick Bars\n"
     ]
    }
   ],
   "source": [
    "print('Creating Dollar Bars')\n",
    "dollar = ml.data_structures.get_dollar_bars('tutorial_data/raw_tick_data.csv', threshold=70000000, batch_size=1000000, verbose=True)\n",
    "\n",
    "print('Creating Volume Bars')\n",
    "volume = ml.data_structures.get_volume_bars('tutorial_data/raw_tick_data.csv', threshold=28000, batch_size=1000000, verbose=False)\n",
    "\n",
    "print('Creating Tick Bars')\n",
    "tick = ml.data_structures.get_tick_bars('tutorial_data/raw_tick_data.csv', threshold=5500, batch_size=1000000, verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Out of Memory?\n",
    "In the event that your dollar bars DataFrame is too large to fit into memory, you will need to save them to a csv file. Simply uncomment the code below to do so."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating Dollar Bars\n",
      "Creating Volume Bars\n",
      "Creating Tick Bars\n"
     ]
    }
   ],
   "source": [
    "print('Creating Dollar Bars')\n",
    "# dollar = ml.data_structures.get_dollar_bars('tutorial_data/raw_tick_data.csv', threshold=70000000, batch_size=1000000, verbose=False, to_csv=True, output_path='./tutorial_data/dollar_bars.csv')\n",
    "\n",
    "print('Creating Volume Bars')\n",
    "# volume = ml.data_structures.get_volume_bars('tutorial_data/raw_tick_data.csv', threshold=28000, batch_size=1000000, verbose=False, to_csv=True, output_path='./tutorial_data/volume_bars.csv')\n",
    "\n",
    "print('Creating Tick Bars')\n",
    "# tick = ml.data_structures.get_tick_bars('tutorial_data/raw_tick_data.csv', threshold=5500, batch_size=1000000, verbose=False, to_csv=True, output_path='./tutorial_data/tick_bars.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirm Sampling:\n",
    "\n",
    "The following cell confrims the sampling techniques are working at the desired thresholds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/01/2013 21:34:39.298</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>1643.5</td>\n",
       "      <td>1639.00</td>\n",
       "      <td>1640.75</td>\n",
       "      <td>42862</td>\n",
       "      <td>70325826.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/02/2013 02:56:24.209</td>\n",
       "      <td>1640.75</td>\n",
       "      <td>1646.0</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>1644.50</td>\n",
       "      <td>42585</td>\n",
       "      <td>70031032.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09/02/2013 06:37:33.128</td>\n",
       "      <td>1644.50</td>\n",
       "      <td>1647.5</td>\n",
       "      <td>1644.25</td>\n",
       "      <td>1647.50</td>\n",
       "      <td>42580</td>\n",
       "      <td>70150550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09/02/2013 09:34:46.141</td>\n",
       "      <td>1647.50</td>\n",
       "      <td>1648.5</td>\n",
       "      <td>1645.25</td>\n",
       "      <td>1647.00</td>\n",
       "      <td>42535</td>\n",
       "      <td>70055145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09/02/2013 22:55:20.297</td>\n",
       "      <td>1647.00</td>\n",
       "      <td>1648.5</td>\n",
       "      <td>1645.25</td>\n",
       "      <td>1648.00</td>\n",
       "      <td>42512</td>\n",
       "      <td>70059776.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date_time     open    high      low    close  volume  \\\n",
       "0  09/01/2013 21:34:39.298  1640.25  1643.5  1639.00  1640.75   42862   \n",
       "1  09/02/2013 02:56:24.209  1640.75  1646.0  1640.25  1644.50   42585   \n",
       "2  09/02/2013 06:37:33.128  1644.50  1647.5  1644.25  1647.50   42580   \n",
       "3  09/02/2013 09:34:46.141  1647.50  1648.5  1645.25  1647.00   42535   \n",
       "4  09/02/2013 22:55:20.297  1647.00  1648.5  1645.25  1648.00   42512   \n",
       "\n",
       "        value  \n",
       "0  70325826.5  \n",
       "1  70031032.5  \n",
       "2  70150550.0  \n",
       "3  70055145.0  \n",
       "4  70059776.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm the dollar sampling\n",
    "dollar['value'] = dollar['close'] * dollar['volume']\n",
    "dollar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date_time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>09/01/2013 19:32:23.387</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>1642.00</td>\n",
       "      <td>1639.00</td>\n",
       "      <td>1642.00</td>\n",
       "      <td>28031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>09/02/2013 01:18:21.928</td>\n",
       "      <td>1642.00</td>\n",
       "      <td>1644.00</td>\n",
       "      <td>1640.25</td>\n",
       "      <td>1643.50</td>\n",
       "      <td>28003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>09/02/2013 02:50:32.992</td>\n",
       "      <td>1643.50</td>\n",
       "      <td>1646.00</td>\n",
       "      <td>1642.25</td>\n",
       "      <td>1644.75</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09/02/2013 04:57:09.236</td>\n",
       "      <td>1644.75</td>\n",
       "      <td>1647.25</td>\n",
       "      <td>1643.75</td>\n",
       "      <td>1646.00</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>09/02/2013 07:04:32.076</td>\n",
       "      <td>1646.00</td>\n",
       "      <td>1648.50</td>\n",
       "      <td>1645.75</td>\n",
       "      <td>1647.50</td>\n",
       "      <td>28013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date_time     open     high      low    close  volume\n",
       "0  09/01/2013 19:32:23.387  1640.25  1642.00  1639.00  1642.00   28031\n",
       "1  09/02/2013 01:18:21.928  1642.00  1644.00  1640.25  1643.50   28003\n",
       "2  09/02/2013 02:50:32.992  1643.50  1646.00  1642.25  1644.75   28000\n",
       "3  09/02/2013 04:57:09.236  1644.75  1647.25  1643.75  1646.00   28000\n",
       "4  09/02/2013 07:04:32.076  1646.00  1648.50  1645.75  1647.50   28013"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Analyze Statistical Properties\n",
    "\n",
    "Now we turn to analyze the statistical properties of the new data structures. For this exercise we have included csv files of the data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read from csv\n",
    "time_bars = pd.read_csv('./tutorial_data/time_bars.csv', index_col=0, parse_dates=True)\n",
    "dollar_bars = pd.read_csv('./tutorial_data/dollar_bars.csv', index_col=0, parse_dates=True)\n",
    "volume_bars = pd.read_csv('./tutorial_data/volume_bars.csv', index_col=0, parse_dates=True)\n",
    "tick_bars = pd.read_csv('./tutorial_data/tick_bars.csv', index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Measure Stability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Downsample to weekly periods\n",
    "time_count = time_bars['price'].resample('W', label='right').count()\n",
    "tick_count = tick_bars['close'].resample('W', label='right').count()\n",
    "volume_count = volume_bars['close'].resample('W', label='right').count()\n",
    "dollar_count = dollar_bars['close'].resample('W', label='right').count()\n",
    "\n",
    "count_df = pd.concat([time_count, tick_count, volume_count, dollar_count], axis=1)\n",
    "count_df.columns = ['time', 'tick', 'volume', 'dollar']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Number of Bars Over Time\n",
    "count_df.loc[:, ['time', 'tick', 'volume', 'dollar']].plot(kind='bar', figsize=[25, 5], color=('darkred', 'darkblue', 'green', 'darkcyan'))\n",
    "plt.title('Number of bars over time', loc='center', fontsize=20, fontweight=\"bold\", fontname=\"Times New Roman\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This stability test will work better on your own tick data over a much longer period. This sample only contains 20 days.\n",
    "\n",
    "### Partial Return to Normality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate log returns\n",
    "time_returns = np.log(time_bars['price']).diff().dropna()\n",
    "tick_returns = np.log(tick_bars['close']).diff().dropna()\n",
    "volume_returns = np.log(volume_bars['close']).diff().dropna()\n",
    "dollar_returns = np.log(dollar_bars['close']).diff().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Statistics:\n",
      "Time: \t 37317\n",
      "Tick: \t 12151\n",
      "Volume:  9107\n",
      "Dollar:  5931\n"
     ]
    }
   ],
   "source": [
    "print('Test Statistics:')\n",
    "print('Time:', '\\t', int(stats.jarque_bera(time_returns)[0]))\n",
    "print('Tick:', '\\t', int(stats.jarque_bera(tick_returns)[0]))\n",
    "print('Volume: ', int(stats.jarque_bera(volume_returns)[0]))\n",
    "print('Dollar: ', int(stats.jarque_bera(dollar_returns)[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the differences\n",
    "time_diff = time_returns\n",
    "tick_diff = tick_returns\n",
    "volume_diff = volume_returns\n",
    "dollar_diff = dollar_returns\n",
    "\n",
    "# Standardize the data\n",
    "time_standard = (time_diff - time_diff.mean()) / time_diff.std()\n",
    "tick_standard = (tick_diff - tick_diff.mean()) / tick_diff.std()\n",
    "volume_standard = (volume_diff - volume_diff.mean()) / volume_diff.std()\n",
    "dollar_standard = (dollar_diff - dollar_diff.mean()) / dollar_diff.std()\n",
    "\n",
    "# Plot the Distributions\n",
    "plt.figure(figsize=(16,12))\n",
    "sns.kdeplot(time_standard, label=\"Time\", color='darkred')\n",
    "sns.kdeplot(tick_standard, label=\"Tick\", color='darkblue')\n",
    "sns.kdeplot(volume_standard, label=\"Volume\", color='green')\n",
    "sns.kdeplot(dollar_standard, label=\"Dollar\", linewidth=2, color='darkcyan')\n",
    "sns.kdeplot(np.random.normal(size=1000000), label=\"Normal\", color='black', linestyle=\"--\")\n",
    "\n",
    "plt.xticks(range(-5, 6))\n",
    "plt.legend(loc=8, ncol=5)\n",
    "plt.title('Exhibit 1 - Partial recovery of Normality through a price sampling process \\nsubordinated to a volume, tick, dollar clock',\n",
    "          loc='center', fontsize=20, fontweight=\"bold\", fontname=\"Times New Roman\")\n",
    "plt.xlim(-5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "The following notebook showed how the mlfinlab package could be used to create new financial data structures that have better statistical properties. The same work can be extended to the information-driven bars which are included in the package. The following links to a notebook that analyzes the properties of [Imbalance Bars](https://github.com/hudson-and-thames/research/blob/master/Chapter2/2019-04-11_OP_Dollar-Imbalance-Bars.ipynb).\n",
    "\n",
    "If you enjoyed this notebook then please be sure to star our repo!\n",
    "* [mlfinlab](https://github.com/hudson-and-thames/mlfinlab)\n",
    "* [research](https://github.com/hudson-and-thames/research)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
